한국지능시스템학회논문지 (Journal of the Korean Institute of Intelligent Systems)

  • 제14권7호
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  • Pages.830-837
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  • 2004
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  • 1976-9172(pISSN)
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  • 2288-2324(eISSN)
한국지능시스템학회 (Korean Institute of Intelligent Systems)
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다층/ART2 신경회로망을 이용한 고장진단

A Fault Diagnosis Based on Multilayer/ART2 Neural Networks

  • 이인수 (상주대학교 전자전기공학부) ;
  • 유두형 (상산전자공업고등학교)
  • 발행 : 2004.12.01
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초록

본 논문에서는 비선형시스템에서 발생한 고장을 감지하고 분류하기 위한 신경회로망기반 고장진단 방법을 제안한다. 제안한 알고리듬에서는 시스템의 출력과 다층신경회로망 공칭모델 출력 사이의 오차가 미리 설정한 문턱값을 넘으면 고장을 감지한다. 고장이 감지되면 다층신경회로망과 ART2 신경회로망을 이용한 고장분류기에서 시스템에서 발생한 고장을 분류한다. 컴퓨터 시뮬레이션 결과로부터 제안한 고장진단방법이 비선형시스템에서의 고장감지 및 분류문제에 잘 적용됨을 알 수 있다.

Neural networks-based fault diagnosis algorithm to detect and isolate faults in the nonlinear systems is proposed. In the proposed method, the fault is detected when the errors between the system output and the multilayer neural network-based nominal model output cross a Predetermined threshold. Once a fault in the system is detected, the system outputs are transferred to the fault classifier by nultilayer/ART2 NN (adaptive resonance theory 2 neural network) for fault isolation. From the computer simulation results, it is verified that the proposed fault diagonal method can be performed successfully to detect and isolate faults in a nonlinear system.

키워드

참고문헌

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피인용 문헌

  1. Implementation of remote monitoring system for prediction of tool wear and failure using ART2 vol.18, pp.1, 2011, https://doi.org/10.1007/s11771-011-0677-7